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cs.CV2026
GLF-Q: Global-Local Feature-based Quantization for Vision Transformers
Peilin Sun, Guang Liang, Jin Tong +1
Post-training quantization (PTQ) efficiently compresses Vision Transformers (ViTs) without retraining, yet suffers severe accuracy degradation at low bit-widths. Existing optimizat…
cs.CV2026
Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
Jin Tong, Guang Liang, Peilin Sun +1
Low-bit quantization is a practical route for efficiently deploying vision Transformers, yet activation outliers complicate fully quantized deployment. Existing methods either hand…
cs.CV2026
Diffusion Model as a Generalist Segmentation Learner
Haoxiao Wang, Antao Xiang, Haiyang Sun +8
Diffusion models are primarily trained for image synthesis, yet their denoising trajectories encode rich, spatially aligned visual priors. In this paper, we demonstrate that these…